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<p>Pseudo-random number generators (PRNGs) are essential for cryptography, secure communications, and simulations. Traditional chaos-based PRNGs suffer from periodicity degradation, inadequate statistical properties, and security vulnerabilities. To address these limitations, this paper proposes a novel PRNG model based on a generative adversarial network (GAN) integrating BiLSTM and Transformer architectures, termed BiLSTM-Transformer-GAN. A hybrid training set constructed from multiple chaotic systems provides rich samples. Within the generator, BiLSTM captures short-term dependencies, a lightweight sparse Transformer enhances long-range features, and chaotic positional encoding improves sensitivity to initial conditions. The discriminator employs parallel multi-branch dilated convolutions to capture multi-scale temporal features. The model adopts the Wasserstein GAN with gradient penalty (WGAN-GP) framework to ensure statistical matching with real chaotic sequences while avoiding mode collapse. Experimental results show that the generated sequences pass all NIST SP 800-22 and TestU01 tests, achieving an information entropy of 7.98 and a Lyapunov exponent of 10.23. Furthermore, by embedding chaotic signals during training, the generator produces sequences in a single forward pass at inference, significantly reducing per-step computational cost compared with traditional iterative chaotic PRNGs. These results validate the model’s superior randomness, chaotic properties, computational efficiency, and cryptographic security, offering an innovative approach for high-security PRNG applications.</p>

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Chaotic pseudo-random number generator enhancement via generative adversarial networks with an integrated BiLSTM-transformer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Xiaobing Liu,
  • Qing Ye,
  • Wei Liu,
  • Qian Zhou,
  • Zebin Song,
  • Guangyue Kou

摘要

Pseudo-random number generators (PRNGs) are essential for cryptography, secure communications, and simulations. Traditional chaos-based PRNGs suffer from periodicity degradation, inadequate statistical properties, and security vulnerabilities. To address these limitations, this paper proposes a novel PRNG model based on a generative adversarial network (GAN) integrating BiLSTM and Transformer architectures, termed BiLSTM-Transformer-GAN. A hybrid training set constructed from multiple chaotic systems provides rich samples. Within the generator, BiLSTM captures short-term dependencies, a lightweight sparse Transformer enhances long-range features, and chaotic positional encoding improves sensitivity to initial conditions. The discriminator employs parallel multi-branch dilated convolutions to capture multi-scale temporal features. The model adopts the Wasserstein GAN with gradient penalty (WGAN-GP) framework to ensure statistical matching with real chaotic sequences while avoiding mode collapse. Experimental results show that the generated sequences pass all NIST SP 800-22 and TestU01 tests, achieving an information entropy of 7.98 and a Lyapunov exponent of 10.23. Furthermore, by embedding chaotic signals during training, the generator produces sequences in a single forward pass at inference, significantly reducing per-step computational cost compared with traditional iterative chaotic PRNGs. These results validate the model’s superior randomness, chaotic properties, computational efficiency, and cryptographic security, offering an innovative approach for high-security PRNG applications.